We evaluate how the system improves by fine-tuning the neural networks with the collected data. For semantic segmentation, we compute the F1 score by comparing the predicted labels with the ground truth labels pixel-wisely. For evaluating 6D poses, we use ADD and ADD-S metric as defined in [1]}, [2]}:
\(\textsc {ADD} = \frac{1}{m}\sum _{\mathbf {x} \in \mathcal {M}}\Vert  (\mathbf {R} \mathbf {x} + \mathbf {t}) - (\mathbf {\tilde{R}} \mathbf {x} + \mathbf {\tilde{t}}) \Vert \) ,
\(\textsc {ADD-S} = \frac{1}{m}\sum _{\mathbf {x}_1 \in \mathcal {M}} \min _{\mathbf {x}_2 \in \mathcal {M}} \Vert  (\mathbf {R} \mathbf {x}_1 + \mathbf {t}) - (\mathbf {\tilde{R}} \mathbf {x}_2 + \mathbf {\tilde{t}}) \Vert \) ,
where \(\mathcal {M}\)  denotes the set of 3D model points and \(m\)  is the number of points. \((\mathbf {R}, \mathbf {t})\)  and \((\mathbf {\tilde{R}}, \mathbf {\tilde{t}})\)  are the ground truth pose and estimated pose, respectively. To evaluate the grasping performance, we compute the success rate, and the speed for initializing pose estimations as well as grasping.
